Buch, Englisch, 310 Seiten, Paperback, Format (B × H): 155 mm x 235 mm, Gewicht: 499 g
Data Driven Methods for Energy Service Innovation
Buch, Englisch, 310 Seiten, Paperback, Format (B × H): 155 mm x 235 mm, Gewicht: 499 g
ISBN: 978-981-1693-62-5
Verlag: Springer Nature Singapore
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Wirtschaftswissenschaften Wirtschaftssektoren & Branchen Energie- & Versorgungswirtschaft
- Mathematik | Informatik Mathematik Operations Research
- Wirtschaftswissenschaften Volkswirtschaftslehre Umweltökonomie
- Mathematik | Informatik EDV | Informatik Daten / Datenbanken Big Data
- Geowissenschaften Umweltwissenschaften Nachhaltigkeit
- Mathematik | Informatik EDV | Informatik Angewandte Informatik
Weitere Infos & Material
Chapter 1 Introduction.- Chapter 2 Residential Electricity Consumption Pattern Mining based on Fuzzy Clustering.- Chapter 3 Load Profiling Considering Shape Similarity using Shape-based Clustering.- Chapter 4 Load Classification and Driven Factors Identification based on Ensemble Clustering.- Chapter 5 Power Demand and Probability Density Forecasting based on Deep Learning.- Chapter 6 Load Forecasting of Residential Buildings based on Deep Learning.- Chapter 7 Incentive-based Demand Response with Deep Learning and Reinforcement Learning.- Chapter 8 Residential Electricity Pricing based on Multi-Agent Simulation.- Chapter 9 Integrated Energy Services based on Integrated Demand Response.- Chapter 10 Electric Vehicle Charging Scheduling Considering Different Charging Demands.- Chapter 11 P2P Electricity Trading Pricing in Energy Blockchain Environment.- Chapter 12 Credit-Based P2P Electricity Trading in Energy Blockchain Environment.